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IEEE International Conference on robotics and Automation (ICrA)

作者： Lorenzo rapetti Yeshasvi Tirupachuri Alberto ranavolo Tomohiro Kawakami Takahide Yoshiike daniele Pucci Dynamic Interaction Control at Istituto Italiano di Tecnologia Center for Robotics Technologies Genova Italy Machine Learning and Optimisation The University of Manchester Manchester United Kingdom Epidemiology and Hygiene INAIL Roma Italy Honda R&D Co. Ltd. Saitama Japan

Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking into account the ergonomic requirements of the robots through the optimization of posture and contact forces. The framework is validated in an experimental scenario with two iCub humanoid robots performing different payload lifting sequences.

关键词： Smoothing methods Ergonomics Force Collaboration Humanoid robots Grasping Torque measurement

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Chargrid-OCr: End-to-end trainable optical character recognition for printed documents using instance segmentation

arXiv

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arXiv 2019年

作者： reisswig, Christian Katti, Anoop r. Spinaci, Marco Höhne, Johannes Machine Learning R&D Berlin Germany

We present an end-to-end trainable approach for Optical Character recognition (OCr) on printed documents. Specifically, we propose a model that predicts a) a two-dimensional character grid (chargrid) representation of a document image as a semantic segmentation task and b) character boxes for delineating character instances as an object detection task. For training the model, we build two large-scale datasets without resorting to any manual annotation - synthetic documents with clean labels and real documents with noisy labels. We demonstrate experimentally that our method, trained on the combination of these datasets, (i) outperforms previous state-of-the-art approaches in accuracy (ii) is easily parallelizable on GPU and is, therefore, significantly faster and (iii) is easy to train and adapt to a new domain. Copyright © 2019, The Authors. All rights reserved.

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Enhancing Alzheimer's disease diagnosis Using Multi-relation Graph Convolutional Networks and Structural MrI data

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. rajesh Kanna C. Gunasundari M. Senthamarai P. Pandiaraja P. Nithin K. Chitra Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Trichy Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Computer Applications Kongu Engineering College Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Alzheimer's disease (Ad) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MrI data and Multi-relation Graph Convolutional Networks (Mr-GCN) to improve Ad diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MrI features. Using a large dataset, the resusts are evaluated and contrasted in Mr-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of Ad. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how Ad progresses.

关键词： Neuroimaging Pathology Accuracy Graph convolutional networks Magnetic resonance imaging machine learning Sensitivity and specificity Brain modeling Alzheimer's disease resilience

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Bond Performance of Embedded reinforcing Steel Bars in Uhpc:A Comprehensive Study

SSRN

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SSrN 2022年

作者： Lee, Kihak Luat, Nguyen-Vu Nguyen, Huu-Cuong Tran, Han V. Yoo, Young Jong Deep Learning Architecture Research Center Department of Architectural Engineering Sejong University 209 Neungdong-ro Gwangjin-gu Seoul05006 Korea Republic of JeongYang SG CO. LTD R&D Center MIT University Vietnam National Highway No. 1A Dau Giay Dong Nai Thong Nhat76000 Viet Nam

This paper presents a comprehensive study on the bond performance of steel bars embedded in UHPC including experimental tests, numerical simulations and a proposed equation for predicting bond strength. A total of 80 specimens with steel bar diameters of 13 or 16 mm, various concrete covers of , , and , and embedded lengths ranging from to were tested by direct pull out. To validate the experimental results, numerical simulations were developed in LS-dYNA to estimate the bond behavior of the tested specimens in terms of failure mode and bond versus slip curves. In addition, a further parametric study on UHPC compressive strength from 150 to 200 Mpa was also performed to propose a new predictive peak bond stress equation. The bond strength values obtained from this equation were in good agreement with the experimental and numerical results, indicating its applicability in practical calculations. © 2022, The Authors. All rights reserved.

关键词： Compressive strength

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research on Graphic design Image Processing Technology Based on Newton's method in Photoshop

Research on Graphic Design Image Processing Technology Based...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Mohammed Hussien Al-Fatlawy M. Sahaya Sheela Santosh Kumar Yadav V Srinivasan S. Gopalakrishnan N V Uma reddy College of Technical Engineering The Islamic University Najaf Iraq Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai Tamil Nadu India Department of Electronics & Communication Engineering IES College Of Technology Bhopal MP India New Prince Shri Bhavani College Of Engineering And Technology Tamilnadu India Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Kelambakkam Tamil Nadu India Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bengaluru

The term "computer graphics and image processing technology" refers to a technology that is based on computers and makes use of software to process graphics and images in a comprehensive manner. Since its introduction, it has been the subject of extensive research and practical use, and as a result, it has steadily evolved into a fully formed and flawless technical system. Image editing, image composition, color correction, the generation of functional color effects, and other related tasks are among the primary features included in Photoshop. The title bar, the menu bar, the picture editing window, the status bar, the toolbox, the control panel, the drawing mode, the file format, the file size, the color setting, shortcut keys, and the color mode are all components of the operational interface. Studying the image processing technology used in graphic design based on NM (Newton's technique) in Photoshop platform is the focus of this research project. Using NM as a foundation, construct a graphic design image processing model. in order to make the global convergence better and reduce the goal function as much as possible. To verify that the goal function is moving in the desired direction, the modified global criterion of NM is applied. According to the findings, the new and improved algorithm has a running time that is 3.67 percent and 2.25% less than that of the NM and the inexact Newton method respectively. The root-mean-square error of the reconstructed image of the cross-section with a diameter of 19mm is controlled to be approximately 5.03%. This error decreases as the amount of Gaussian noise increases. Even if there is significant interference from Gaussian noise when the algorithm reconstructs sections of varying diameters, the quality of the reconstructed image of the improved NM has only a little amount of impact on the reconstructed image.

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A Multimodal Assessment Framework for Integrating Student Writing and drawing in Elementary Science learning

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IEEE TrANSACTIONS ON learning TECHNOLOGIES 2019年第1期12卷 3-15页

作者： Smith, Andy Leeman-Munk, Samuel Shelton, Angi Mott, Bradford Wiebe, Eric Lester, James North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA SAS Inst Inc Artificial Intelligence & Machine Learning R&D Cary NC 27513 USA North Carolina State Univ Coll Educ Raleigh NC 27695 USA

Science learning is inherently multimodal, with students utilizing both drawings and writings to explain observations of physical phenomena. As such assessments in science should accommodate the many ways students express their understanding, especially given evidence that understanding is distributed across both drawing and writing. In recent years advanced automated assessment techniques that evaluate expressive student artifacts have emerged. However, these techniques have largely operated individually, each considering only a single mode. We propose a framework for the multimodal automated assessment of students' writing and drawing to leverage the synergies inherent across modalities and create a more complete and accurate picture of a student's knowledge. We introduce a multimodal assessment framework as well as two computational techniques for automatically analyzing student writings and drawings: a convolutional neural network-based model for assessing student writing, and a topology-based model for assessing student drawing. Evaluations with elementary students' writings and drawings collected with a tablet-based digital science notebook demonstrate that 1) each of the framework's two-modalities provide an independent and complementary measure of student science learning, and 2) the computational methods are capable of accurately assessing student work from both modalities and offer the potential for integration in technology-rich learning environments for real-time formative assessment.

关键词： Intelligent tutoring systems formative assessment multimodal assessment student writing analysis student drawing analysis

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Scalable stellar evolution forecasting: deep learning emulation vs. hierarchical nearest neighbor interpolation

arXiv

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arXiv 2023年

作者： Maltsev, K. Schneider, F.r.N. röpke, F.K. Jordan, A.I. Qadir, G.A. Kerzendorf, W.E. riedmiller, K. van der Smagt, P. Heidelberger Institut für Theoretische Studien Schloss-Wolfsbrunnenweg 35 HeidelbergD-69118 Germany Zentrum für Astronomie Der Universität Heidelberg Institut für Theoretische Astrophysik Philosophenweg 12 HeidelbergD-69120 Germany Machine Learning Research Lab Volkswagen AG Munich Germany Faculty of Informatics Eötvös Loránd University Budapest Hungary Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Zentrum für Astronomie Der Universität Heidelberg Astronomisches Rechen-Institut Mönchhofstr. 12-14 HeidelbergD-69120 Germany

Many astrophysical applications require efficient yet reliable forecasts of stellar evolution tracks. One example is population synthesis, which generates forward predictions of models for comparison with observations. The majority of state-of-the-art rapid population synthesis methods are based on analytic fitting formulae to stellar evolution tracks that are computationally cheap to sample statistically over a continuous parameter range. The computational costs of running detailed stellar evolution codes, such as MESA, over wide and densely sampled parameter grids are prohibitive, while stellar-age based interpolation in-between sparsely sampled grid points leads to intolerably large systematic prediction errors. In this work, we provide two solutions for automated interpolation methods that offer satisfactory trade-off points between cost-efficiency and accuracy. We construct a timescale-adapted evolutionary coordinate and use it in a two-step interpolation scheme that traces the evolution of stars from zero age main sequence all the way to the end of core helium burning while covering a mass range from 0.65 to 300 M☉. The feedforward neural network regression model (first solution) that we train to predict stellar surface variables can make millions of predictions, sufficiently accurate over the entire parameter space, within tens of seconds on a 4-core CPU. The hierarchical nearest-neighbor interpolation algorithm (second solution) that we hard-code to the same end achieves even higher predictive accuracy, the same algorithm remains applicable to all stellar variables evolved over time, but it is two orders of magnitude slower. Our methodological framework is demonstrated to work on the MESA Isochrones and Stellar Tracks (Choi et al. 2016) data set, but is independent of the input stellar catalog. Finally, we discuss the prospective applications of these methods and provide guidelines for generalizing them to higher dimensional parameter spaces. Copyright © 202

关键词： Forecasting

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Cloud-based data Analytics for Automated Coastal Cleanup robots with Convolutional Neural Network

Cloud-based Data Analytics for Automated Coastal Cleanup Rob...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： A. ramesh Babu Chethan Chandra S Basavaraddi ramesh S S K Mouleeswaran Santosh Kumar Sahoo P. Solainayagi School of Computer Applications Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India Department of Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology Bangalore VTU Belagavi Karnataka India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Engineering and Technology VIT University Bhopal Madhya Pradesh India Department of Computer Science and Engineering Dayananda Sagar University Bangalore Karnataka India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The growing danger of coastal pollution requires novel, automated remediation methods. This paper proposes combining autonomous robots with sophisticated sensor technologies and a Convolutional Neural Network (CNN)-based data analytics system to clean up coastal areas. The robots autonomously cruise coastal settings, discover and categories marine waste using onboard sensors, and send data to a cloud-based infrastructure for analysis. The proposed method relies on CNN, a deep learning model known for image recognition. For reliable identification and classification, CNN is trained on a broad collection of marine waste images. The cloud-based data analytics platform receives, processes, and stores cleaning robot data. It handles massive datasets effectively using cloud computing scalability and processing power. The technology uses real-time cloud image processing to identify garbage and enable robots to adjust their cleaning tactics. The data analytics platform generates detailed reports and visualizations to analyze cleaning efficiency and environmental effect. It advances automated coastal cleaning technology by integrating advanced robotics, sensor technologies, and deep learning. The proposed approach improves coastal cleaning speed, accuracy, and flexibility to protect marine habitats and biodiversity.

关键词： deep learning Cloud computing data analysis Pollution Accuracy Sea measurements robot sensing systems Cleaning real-time systems Convolutional neural networks

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Smart Last-Mile delivery with IoT-Connected robot Vehicles

Smart Last-Mile Delivery with IoT-Connected Robot Vehicles

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International Conference on Smart Computing & Communications (ICSCC)

作者： M Sujatha rajesh Kambattan Kovarasan K. Sudha Nilamadhab Mishra G Thiyagarajan C. Srinivasan Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Business Administration STET Women's College (Autonomous) Mannargudi Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

This research discusses using IoT technologies with robot vehicles to improve last-mile deliveries. This novel method optimizes the last stage of the distribution chain, which sometimes presents logistical issues. The system examines robot vehicles' last-mile delivery monitoring, data collecting, and communication capabilities via an Internet of Things connection. These trucks collect and communicate traffic, packaging, and environmental data using sensors and communication equipment. Analyzing and processing this data improves route planning, delivery timing, and performance. Through the IoT, autonomous delivery trucks, a centralized control center, and other components of the delivery ecosystem could operate in combination. This collaborative network helps respond to changing situations such as highway congestion and package urgency by rerouting or raising delivery priority. The potential for delivery trucks to improve productivity, intelligence, and focus on the customer in the last mile of a package's journey. The IoT-driven solutions may be highlighted as major results, advantages, and implications for the logistics sector, addressing urbanization, consumer demands, and environmental concerns in modern delivery systems.

关键词： Accuracy Urban areas Transforms Medical services robot sensing systems Timing Electronic commerce Sustainable development Monitoring Logistics

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An Enhanced Traffic Incident detection using Factor Analysis and Weighted random Forest Algorithm

An Enhanced Traffic Incident Detection using Factor Analysis...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： P. rajesh Kanna S. Vanithamani P. Karunakaran P. Pandiaraja N. Tamilarasi P. Nithin Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Master of Computer Applications M. Kumarasamy College of Engineering Karur Tamil Nadu India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Nehru Institute of Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Efficient and precise traffic incident detection is essential to reduce casualties and property damage. To address the issue of unbalanced event data, this work offers a novel methodology known as FA-WrF (Factor Analysis and Weighted random Forest). This approach combines dimensionality reduction through factor analysis with classification using weighted random forests, data preparation through the Synthetic Minority Over-Sampling Technique (SMOTE), and data preparation with SMOTE. The included feature of severity detection is highlighted, as are the performance metrics of the FA-WrF model using well-established metrics such as detection level rate, number of false count alarm rate, classification type rate, and area specification under the receiver operating characteristic curve (AUC). The superiority of the FA-WrF model is illustrated using real-world expressway traffic data represented by imbalanced incidents through thorough comparisons with various machine learning-based algorithms such as Support Vector machine, k-nearest neighbours algorithms, Logistic regression algorithms, and decision-making trees. In addition to advancing incident detection, this technique has encouraging prospects for enhancing traffic management procedures and well-informed decision-making procedures in transportation networks.

关键词： Measurement Support vector machines Logistic regression machine learning algorithms decision making Transportation receivers data models Classification algorithms random forests

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